Data Mining Within The Retail Business World{0}


By Andrew R.

Across various business, data mining forms a crucial part of the decision-making process when it comes to steering a firm in a direction that is according to the information they have about their products & services as well as their customers. In the retail world, this involves collecting information on products, their customers, & the relationships between those two entities so that they may better be able to adapt the business to ongoing trends. Companies that can craft accurate models for themselves give themselves a competitive advantage when it comes to oversight over their business.

What makes data so valuable? when it comes to business, management must make decisions and plans based on the information they have. The safest way to ensure they are making the right decision is to take a look at past trends such as what has sold the most, what time of the year customers prefer, etc. By being able to better predict the needs of the store, they can be prepared for adapting when necessary. A retail store that knows it will only need 400-600 of a product they sold last month, knows that buying 1200 will leave a high amount of inventory which reflects waste, by matching demand they will save themselves some money. By processing customer feedback, companies may also change to increase customer satisfaction and by extension increase customer retention.

One of the largest ongoing trends within retail databasing in the last decade has foremost been online retailers who lack the traditional physical infrastructure that physical stores have. In terms of data mining, online is much stronger at tracking the habits of individual customers, such as Amazon who is able to even recommend products to their customers based on their traditional spending habits. “Location-based marketing” is the brick and mortar reply to online data hoarders (Hui, 2015). In 2014 Nordstrom was using wi-fi tracking within their stores until privacy issues forced them to cancel the policy, however, by tracking the favored areas of their stores, Nordstrom was able to see which products received more attention from their customer base. Newer, less intrusive innovations on customer tracking include specialized apps that allow the customer to view special sales and deals when downloaded via bluetooth, this gives the customer a choice in downloading the app. Retailers that market campaigns based on customer data are statistically more effective than generalized marketing campaigns that target everyone (Hennel 2014).

Observing personal habits of individuals can be considered invasive & a breach of privacy, while firms may make state they are doing ii in order to improve themselves and be of most use to the customer, some sensitive data, like credit card information and addresses can put the customer at risk. In 2016 the Playstation online retail network was hacked which released thousands of credit card information to malicious hackers (Hui 2015). Setbacks such as these reflect the danger of unsecure data management that comes with storing data on customers. If a company stores data on their customers then ethically speaking they are obligated to protect that trust between themselves and the customer (Katsov, 2015).

Customer data aside, one of the most important aspects of retail and data mining within retail is its ability to reflect the actions of proper supply chain management. Earlier there was an example of how a store needs to order a certain number of products in order to avoid wasteful sitting inventory. When retailers need to procure their inventory, they need efficient inventory control which is performed automatically via bar codes. Previously, inventory needed to be managed manually through counting but with the automated inventory system a retailer can immediately and automatically request more goods even before they realize they need the additional goods.

Overall data mining is a very dynamic endeavor for a retailer to pursue but with the speed that retailers are required to operate in order to stay competitive, data mining is an essential asset to operations management. Retailers have the potential to identify their customers’ needs as well as optimize their products and how they present the products to those customers. Models constructed off existing trends ultimately prove to the best option for making decisions long before events have occurred.

References
Katsov, I. (2015, March 17). Data Mining Problems in Retail. Retrieved February 16, 2017, from https://highlyscalable.wordpress.com/2015/03/10/data-mining-problems-in-retail/

Hennel, P. (2014, March 04). Why Retailers Should Care About Data Mining. Retrieved February 16, 2017, from https://www.silvon.com/blog/retailers-data-mining/

Hui, R. (2015, July 16). The Future Of Retail Won’t Be So Good For Consumers. Retrieved February 16, 2017, from https://techcrunch.com/2015/07/16/the-future-of-retail-wont-be-so-good-for-consumers/

Miners, Z. (2013, December 20). In 2014, more retailers may analyze how you shop. Retrieved . . February 16, 2017, from http://www.pcworld.com/article/2082600/in-2014-more-retailers-might-know-how-you-shop.html